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Revolutionizing Load Balancing in Cloud Computing With Genetic Algorithms
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Author(s): Abhipsha Das (School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India), Swayam Yadav (School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India), Neetu Dey (School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India), Aayushma Gautam (School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India)and Hitesh Mohapatra (School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India)
Copyright: 2026
Pages: 22
Source title:
Cloud Computing's Transformative Power in Computing Environments
Source Author(s)/Editor(s): Aisha Hassan Abdalla Hashim (International Islamic University, Malaysia), Zeinab E. Ahmed (University of Gezira, Sudan), Othman O. Khalifa (International Islamic University, Malaysia)and Rashid A. Saeed (Taif University, Saudi Arabia)
DOI: 10.4018/979-8-3693-9984-2.ch013
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Abstract
Load balancing is vital in cloud computing for efficiently distributing workloads and preventing resource bottlenecks. This review explores using Genetic Algorithms (GAs), known for their optimization strength, to improve load balancing by minimizing task execution times and boosting resource utilization. The GA-based approach adapts to changing tasks and conditions, evolving solutions through iterative natural selection processes. This paper examines the key principles and real-world applications of this method, showing its potential to transform traditional load balancing and enhance cloud system performance and scalability.
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